Health octo tool matches personalized health with rate of aging

Abstract

Medical practice mainly addresses single diseases, neglecting multimorbidity as a heterogeneous health decline across organ systems. Aging is a multidimensional process and cannot be captured by a single metric. Therefore, we assessed global health in longitudinal studies, BLSA (n = 907), InCHIANTI (n = 986), and NHANES (n = 40,790), by examining disease severities in 13 bodily systems, generating the Body Organ Disease Number (BODN), reflecting progressive system morbidities. We used Bayesian ordinal models, regressing BODN over organ specific and all organs disease severities to obtain Body System-Specific Clocks and the Body Clock, respectively. The Body Clock is BODN weighted by the posterior coefficient of diseases for each individual. It supersedes the frailty index, predicting disability, geriatric syndrome, SPPB, and mortality with ≥90% accuracy. The Health Octo Tool, derived from Bodily System-Specific Clocks, the Body Clock and Clocks that incorporate walking speed and disability and their aging rates, captures multidimensional aging heterogeneity across organs and individuals.

Fig. 1. The Health Octo Tool is comprised of eight components designed to assess multidimensional health.

The Clock components of the Health Octo Tool. Body Organ Disease Number (BODN) quantifies organ systems with at least one disease, considering diseases across multiple systems. Post hoc analyses of the organ-specific diseases predicting BODN yield the Bodily Organ-Specific Clock (BSC). Including all organ systems in the Bayesian ordinal regression generates the Body Clock. The Body Clock impacts walking speed, resulting in the Speed-Body Clock, and functional and cognitive disability, resulting in the Disability-Body Clock. System abbreviations: CNS Central Nervous System, St Stroke, CV Cardiovascular, Thyr Thyroid, MS Musculoskeletal, He Hematology, Pe Periodontal system, Ca Cancer, Se Sensory, GI-Liv Gastrointestinal and Liver system, Me Metabolic system, Res Respiratory system, Re Renal System. The interactive sunburst graphs shown at https://bodiagesystem.shinyapps.io/BODN_BLSA/ reveal increases in multisystem morbidity and BODN values with aging and heterogenous combinations of systems in the BLSA data. Sample size n = 907.

Fig. 2. Model weight comparisons using average model stacking.

Model weights for single diseases in the BLSA at time 1 (A) and time 2 (B), single systems (C, D), and multisystem (E, F) are compared to the corresponding age-only model weights. The data illustrate that the weight of single systems surpasses that of single diseases. As the number of systems increases, indicating a rise in entropy, the weight of multisystem models significantly surpasses that of chronological age, enabling a more optimal model for the prediction of longitudinal BODN. Sample size n = 907, Women = 451. The detailed weights are reported in Table S7.

Fig. 3. Relationships between bodily system-specific age (BSA) and chronological age.

The graph reveals that BSA for cardiovascular (CV), renal (Re), Res, CNS, and MS systems can exceed 120, surpassing the maximum reported human chronological age. System abbreviations same as Fig. 1. Sample size n = 907, Women = 451.

Fig. 4. Lagged full-model multisystem posterior estimates predict longitudinal body organ disease number (BODN).

The lagged full-model estimates reveal heterogenous contributions of diseases into BODN. A Maximum contribution of diseases into longitudinal BODN at time 1. B Maximum contributions of diseases into longitudinal BODN at time 2. Larger posterior estimates, accompanied by narrower 95% credible intervals (CI), yield more accurate predictions. The maximum effects of most diseases increase with time. Significance is determined when the 95% CI does not include 0. Disease Abbreviations: HTN hypertension, IHD ischemic heart disease, CHF congestive heart failure, Arr arrhythmia, PAD peripheral artery disease, CKD chronic kidney disease, DM diabetes mellitus, GID gastrointestinal disease, COPD chronic obstructive pulmonary disease, Hypoth hypothyroidism, Hyperth hyperthyroidism, OA osteoarthritis, Osteop osteoporosis, Periodon periodontal disease, Dep depression, Park Parkinson’s disease, CI cognitive impairment. System abbreviations: same as for Fig. 1. For the values of mean posterior estimates and 95% CI, please see the values in Table S6C, F for full models. Sample size n = 907, Women = 451.

Fig. 5. Disability-body age and chronological age in BLSA datasets.

Disability-Body Age surpasses the reported chronological age in humans. There is increasing heterogeneity in Disability-Body Age with increases in chronological age. Sample size n = 907, Women=451.

Fig. 6. High correlation between the Body Clock and disease-based frailty index (FI) with BLSA (r = 0.83, 95% CI: 0.816–0.84).

Body Clock also captures heterogeneity better than FI: for heterogeneous values of the Body Clock—serving as a proxy for the intrinsic age and health entropy—FI values remain largely unchanged. Sample size n = 907, Women = 451.

Fig. 7. Receiver operating characteristic (ROC) and area under the curve (AUC) of the Body Clock and FI score predict binary outcomes in the BLSA data.

The Body Clock predicts binary SPPB < 9, geriatric syndrome, and disability, superseding the FI score with more than 90% accuracy. Sample size n = 907, Women=451.

Fig. 8. There is a high correlation between in-data and out-of-data methods used developing the Body Clock in InCHIANTI study.

With the in-data method the full model (all diseases predicting longitudinal BODN) was performed, and the Body Clock (replication) was obtained. Using the out-of-data method the parameters of the BLSA model (training set) were applied to the InCHIANTI data (test set). The graph depicts a high correlation between in-data and out-of-data methods of developing the Body Clock. The Pearson correlation for men was r = 0.76, 95% CI (0.73–0.78) and for women was r = 0.70, 95% CI (0.68–0.72). The parameters of a valid model can be used to predict new data. Sample size in InCHIANTI data n = 986, women = 551.